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tidyversediagrams
0.0.0.9000
ggplot2
dplyr
tidyr
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purrr
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Reference
Exported functions
A
add_case
add_column
add_row
as.tibble
as_data_frame
as_tibble
as_tibble_col
as_tibble_row
C
column_to_rownames
D
data_frame
data_frame_
deframe
E
enframe
F
frame_data
frame_matrix
G
glimpse
H
has_rownames
I
is.tibble
is_tibble
K
knit_print.trunc_mat
L
lst
lst_
N
new_tibble
R
remove_rownames
repair_names
rowid_to_column
rownames_to_column
S
set_tidy_names
T
tbl_sum
tibble
tibble_
tibble_row
tidy_names
tidy_names
is_syntactic
make_syntactic
append_pos
describe_tidying
tribble
trunc_mat
V
validate_tibble
view
Unexported functions
-
$.tbl_df</h3> <p><img src="data:image/png;base64,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" /></p> </div> <div id="tbl_df-1" class="section level3"> <h3>$
<-.tbl_df
[.tbl_df
[[.tbl_df
[[<-.tbl_df
[<-.tbl_df
A
accumulate
accumulate
f
accumulate_right
accumulate_right
f
add_to_env
add_to_env2
args_recycle
args_recycle
1
as.data.frame.tbl_df
as_friendly_type
as_tibble.data.frame
as_tibble.default
as_tibble.list
as_tibble.matrix
as_tibble.matrix
.name_repair
as_tibble.NULL
as_tibble.poly
as_tibble.table
as_tibble.ts
as_tibble_deep
B
big_mark
bullets
C
cells_to_col_idx
check_all_lengths_one
check_minimal
check_minimal_names
check_names_before_after
check_names_before_after_character
check_needs_no_dim
check_valid_col
check_valid_cols
cnd_names_non_na
cnd_names_non_null
cnd_signal_if
coalesce2
col_lengths
collapse
commas
compact
compat_as_lazy
compat_as_lazy_dots
compat_lazy
compat_lazy_dots
compat_lazy_dots
1
compat_name_repair
D
detect
detect_dot_dot
detect_duplicates
detect_empty_names
detect_index
dim_desc
discard
E
ensure_full_stop
error_add_rows_to_grouped_df
error_already_has_rownames
error_as_tibble_row_bare
error_as_tibble_row_size_one
error_assign_columns_non_missing_only
error_assign_columns_non_na_only
error_assign_incompatible_size
error_assign_incompatible_type
error_assign_rows_non_na_only
error_both_before_after
error_column_names_cannot_be_dot_dot
error_column_names_cannot_be_empty
error_column_names_must_be_unique
error_column_scalar_type
error_dim_column_index
error_duplicate_column_subscript_for_assignment
error_duplicate_row_subscript_for_assignment
error_enframe_has_dim
error_enframe_value_null
error_frame_matrix_list
error_glimpse_infinite_width
error_incompatible_new_cols
error_incompatible_new_rows
error_incompatible_size
error_incompatible_size
1
error_na_column_index
error_names_must_be_non_null
error_names_must_have_length
error_need_rhs_vector
error_need_rhs_vector_or_null
error_new_columns_at_end_only
error_new_rows_at_end_only
error_new_tibble_must_be_list
error_new_tibble_needs_nrow
error_subset_columns_non_missing_only
error_subset_matrix_must_be_logical
error_subset_matrix_must_be_scalar
error_subset_matrix_must_have_same_dimensions
error_subset_matrix_scalar_type
error_tibble_row_size_one
error_tribble_c
error_tribble_lhs_column_syntax
error_tribble_needs_columns
error_tribble_non_rectangular
error_tribble_rhs_column_syntax
error_unknown_column_names
every
extract_frame_data_from_dots
extract_frame_names_from_dots
F
fast_nrow
fix_oob
fix_oob_invalid
fix_oob_negative
fix_oob_positive
foreign_caller_env
format.tbl
format.trunc_mat
format_body
format_comment
format_extra_vars
format_header
format_knitr_body
format_n
format_v
format_v.character
format_v.default
format_v.factor
format_v.list
friendly_type_of
G
glimpse.data.frame
glimpse.data.frame
1
glimpse.default
glimpse.tbl
glimpse.tbl
1
guess_nrow
H
has_null_names
has_tibble_arg
I
imap
index
invalid_df
is_rstudio
is_syntactic
is_syntactic_impl
is_tight_sequence_at_end
is_valid_col
J
justify
K
keep
L
limit_pos_range
lst_quos
lst_to_tibble
M
make_valid_col
map
map_chr
map_cpl
map_dbl
map_if
map_int
map_lgl
map_mold
map2
map2_chr
map2_cpl
map2_dbl
map2_int
map2_lgl
matrix_to_cells
matrix_to_cells
1
matrixToDataFrame
mult_sign
N
na_value
name_or_pos
names<-.tbl_df
nchar_width
needs_dim
needs_list_col
negate
negate
1
new_data_mask_with_data
P
pluck
pluck_chr
pluck_cpl
pluck_dbl
pluck_int
pluck_lgl
pluralise
pluralise_commas
pluralise_count
pluralise_msg
pluralise_n
pmap
pos_from_before_after
pos_from_before_after_names
pre_dots
print.tbl
print.trunc_mat
print_without_body
print_without_body
1
probe
problems
Q
quote_n
quote_n.character
quote_n.default
R
raw_rownames
rbind_at
recycle_columns
reduce
reduce
f
reduce_right
reduce_right
f
register_if_pillar_hasnt
repaired_names
replace_if_pillar_has
result_vectbl_wrap_rhs
row.names<-.tbl_df
S
safe_match
safe_match_3_0
safe_match_default
same_as_tbl
same_as_tbl_code
same_as_tbl_code
1
scoped_lifecycle_errors
scoped_lifecycle_silence
scoped_lifecycle_warnings
set_default_name
set_dftbl_chunk_hook
set_dftbl_chunk_hook
dftbl_chunk_hook
set_dftbl_error_hook
set_dftbl_error_hook
dftbl_error_hook
set_dftbl_hooks
set_dftbl_knit_hook
set_dftbl_knit_hook
dftbl_knit_hook
set_dftbl_opts_hook
set_dftbl_opts_hook
dftbl_opts_hook
set_dftbl_source_hook
set_dftbl_source_hook
dftbl_source_hook_one
dftbl_source_hook
set_dftbl_warning_hook
set_dftbl_warning_hook
dftbl_warning_hook
set_repaired_names
set_tibble_class
set_tibble_subclass
shrink_mat
signal_superseded
size_sum
some
spaces_around
splice_dfs
splice_dfs
1
split_lines
str.tbl_df
str_trunc
string_to_indices
strwrap2
subclass_col_index_errors
subclass_col_index_errors
1
subclass_name_repair_errors
subclass_name_repair_errors
1
2
3
subclass_row_index_errors
subclass_row_index_errors
1
subclass_tribble_c_errors
subclass_tribble_c_errors
1
T
tbl_expand_to_nrow
tbl_subassign
tbl_subassign_col
tbl_subassign_matrix
tbl_subassign_matrix
1
tbl_subassign_row
tbl_subassign_row
1
tbl_subset_col
tbl_subset_matrix
tbl_subset_row
tbl_subset2
tbl_sum.default
tbl_sum.tbl
tibble_error
tibble_error_class
tibble_glimpse_width
tibble_opt
tibble_quos
tibble_width
tick
tick_if_needed
transpose
transpose
1
turn_frame_data_into_frame_matrix
turn_frame_data_into_tibble
turn_matrix_into_column_list
type_sum.tbl_df
U
update_tibble_attrs
use_repair
V
validate_nrow
validate_rectangular_shape
vecpurrr_index
vectbl_as_col_location
vectbl_as_col_location2
vectbl_as_col_subscript2
vectbl_as_new_col_index
vectbl_as_new_row_index
vectbl_as_row_index
vectbl_as_row_location
vectbl_as_row_location2
vectbl_recycle_rhs
vectbl_recycle_rhs
1
vectbl_recycle_rhs_names
vectbl_recycle_rows
vectbl_restore
vectbl_strip_names
vectbl_wrap_rhs_col
vectbl_wrap_rhs_row
W
walk
warn_text_se
warn_underscored
with_lifecycle_errors
with_lifecycle_silence
with_lifecycle_warnings
wrap
Contents